Rank order-based recommendation approach for multiple featured products
- Authors
- Choi, Sang Hyun; Ahn, Byeong Seok
- Issue Date
- Jun-2011
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Personalized recommendation; Ordinal weight; Similarity measure; Multi-attribute value
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS, v.38, no.6, pp 7081 - 7087
- Pages
- 7
- Journal Title
- EXPERT SYSTEMS WITH APPLICATIONS
- Volume
- 38
- Number
- 6
- Start Page
- 7081
- End Page
- 7087
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/21475
- DOI
- 10.1016/j.eswa.2010.12.062
- ISSN
- 0957-4174
1873-6793
- Abstract
- Recommendation methods, which suggest a set of products likely to be of interest to a customer, require a great deal of information about both the user and the products. Recommendation methods take different forms depending on the types of preferences required from the customer. In this paper, we propose a new recommendation method that attempts to suggest products by utilizing simple information, such as ordinal specification weights and specification values, from the customer. These considerations lead to an ordinal weight-based multi-attribute value model. This model is well suited to situations in which there exist insufficient data regarding the demographics and transactional information on the target customers, because it enables us to recommend personalized products with a minimal input of customer preferences. The proposed recommendation method is different from previously reported recommendation methods in that it explicitly takes into account multidimensional features of each product by employing an ordered weight-based multi-attribute value model. To evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures. (C) 2010 Elsevier Ltd. All rights reserved.
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Collections - College of Business & Economics > School of Business Administration > 1. Journal Articles
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